Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity

• Published in: Human brain mapping 2018-04, Vol.39 (4), p.1777-1788
• Main Authors: de Pierrefeu, Amicie, Fovet, Thomas, Hadj‐Selem, Fouad, Löfstedt, Tommy, Ciuciu, Philippe, Lefebvre, Stephanie, Thomas, Pierre, Lopes, Renaud, Jardri, Renaud, Duchesnay, Edouard
• Format: Article
• Language: English
• Subjects: Adult; Auditory Perception - physiology; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain mapping; Brain Mapping - methods; Classification; Computerized Image Analysis; datoriserad bildanalys; Feedback; Female; Functional magnetic resonance imaging; Hallucinations; Hallucinations - diagnostic imaging; Hallucinations - physiopathology; Human health and pathology; Humans; Image classification; Learning algorithms; Life Sciences; Machine Learning; Magnetic Resonance Imaging - methods; Male; Medical innovations; Mental disorders; Neural Pathways - diagnostic imaging; Neural Pathways - physiopathology; Neurofeedback; Pattern Recognition, Automated - methods; Predictions; Principal Component Analysis; Principal components analysis; Psychiatrics and mental health; real-time fMRI; resting-state networks; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - physiopathology; Sparsity; Spatial analysis; Statistics; Support vector machines; Variability
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23953

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time‐consuming. This article first proposes a machine‐learning algorithm to automatically identify resting‐state fMRI periods that precede hallucinations versus periods that do not. When applied to whole‐brain fMRI data, state‐of‐the‐art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech‐related brain regions. The variation in transition‐to‐hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI‐guided therapy for drug‐resistant hallucinations, such as fMRI‐based neurofeedback.